检索结果-内蒙古大学图书馆

IEEE International Conference on Robotics and Biomimetics (ROBIO 2007)

作者： Mendes, Mateus Coimbra, A. Paulo Crisostomo, Manuel Univ Coimbra Dept Elect & Comp Engn ISR Inst Syst & Robot ESTGOHIPC Coimbra P-3000 Coimbra Portugal

ISBN: (纸本)9781424417612

Traditional approaches to Artificial Intelligence (AI) and Robotics seem only to provide advances at a very slow pace. Many researchers agree that new, different approaches are needed to provide a breakthrough and allow the construction of robots with human-like capacities. Our approach consists in navigating a robot using vision a sparse distributed memory (SDM), a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. During learning the robot will store sequences of images in the SDM. During execution the robot will follow the sequence of images that is closest to its current view. Preliminary results show that the memory can store and predict sequences of images with a small error tolerance.

关键词： robot navigation view sequence sparse distributed memory

来源：评论

学校读者我要写书评

暂无评论

Scalable realization of sparse distributed memory on a multiprocessor system 9

Scalable realization of Sparse Distributed Memory on a multi...

引用

9th International Conference on Intelligent Systems

作者： Kolinummi, P Tikkanen, KN Hämäläinen, T Saarinen, J Tampere Univ Technol Digital & Comp Syst Lab FIN-33101 Tampere Finland

ISBN: (纸本)1880843331

Parallel mappings of sparse distributed memory (SDM) are presented for a partial tree shape neurocomputer. The implemented partial tree shape neurocomputer called PARNEU is a scalable co-processor system to speed up soft computing applications. The used SDM algorithm benefits from the improved reading method and mask based activation. Two different mapping strategies are presented and analysed in detail The effect of SDM size is analysed and the system scalability is verified with speedup figures of different SDM networks. Almost linear speedup is achieved with reasonable size SDM algorithm.

关键词： sparse distributed memory neurocomputer parallel mapping

来源：评论

学校读者我要写书评

暂无评论

Robot navigation using a sparse distributed memory

Robot navigation using a Sparse Distributed Memory

引用

IEEE International Conference on Robotics and Automation

作者： Mendes, Mateus Crisotomo, Manuel Coimbra, A. Paulo IPC Coimbra ESTGOH Coimbra Portugal Univ Coimbra ISR Dept Elect & Comp Engn Coimbra Portugal

ISBN: (纸本)9781424416462

Despite all the progress that has been made in Robotics and Artificial Intelligence, traditional approaches seem unsuitable to build truly intelligent robots, exhibiting human-like behaviours. Many authors agree that the source of intelligence is, to a great extent, the use of a huge memory, where sequences of events that guide our later behaviour are stored. Inspired by that idea, our approach is to navigate a robot using sequences of images stored in a sparse distributed memory-a kind of associative memory based on the properties of high dimensional binary spaces, which, in theory, exhibits some human-like behaviours. The robot showed good ability to correctly follow most of the sequences learnt, with small errors and good immunity to the kidnapped robot problem.

关键词： robot navigation view sequence sparse distributed memory

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient and High Throughput sparse distributed memory Architecture

Energy-efficient and High Throughput Sparse Distributed Memo...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Kang, Mingu Kim, Eric P. Keel, Min-sun Shanbhag, Naresh R. Univ Illinois Dept Elect & Comp Engn 1406 W Green St Urbana IL 61801 USA

ISBN: (纸本)9781479983919

This paper presents an energy-efficient VLSI implementation of sparse distributed memory (SDM). High throughput and energy-efficient Hamming distance-based address decoder (CM-DEC) is proposed by employing compute memory [1], where computation is deeply embedded into a memory (SRAM). Hierarchical binary decision (HBD) is also proposed to enhance area- and energy-efficiency of read operation by minimizing data transfer. The SDM is employed as an auto-associative memory with four read iterations and 16 x 16 binary noisy input image with input error rates of 15%, 25%, and 30%. The proposed SDM achieves 39x smaller energy delay product with 14.5x and 2.7x reduced delay and energy, respectively as compared to conventional digital implementation of SDM in 45 nm SOI CMOS process with output error rate degradation less than 0.4%.

关键词： Associative memory Compute memory Machine learning Pattern recognition sparse distributed memory

来源：评论

学校读者我要写书评

暂无评论

Extended sparse distributed memory

Extended Sparse Distributed Memory

引用

2nd Annual Meeting of the Biologically-Inspired-Cognitive-Architectures-Society (BICA)

作者： Snaider, Javier Franklin, Stan Univ Memphis Inst Intelligent Syst Dept Comp Sci Memphis TN 38152 USA

ISBN: (纸本)9781607509592;9781607509585

sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM that uses word vectors of larger size than address vectors. This extension preserves many of the desirable properties of the original SDM: auto-associability, content addressability, distributed storage, robustness over noisy inputs. In addition, it adds new functionality, enabling an efficient auto-associative storage of sequences of vectors, as well as of other data structures such as trees.

关键词： sparse distributed memory Episodic memory Sequence Representation Cognitive Modeling

来源：评论

学校读者我要写书评

暂无评论

SDM-Go: an Agent for Go with an Improved Search Process Based on Monte-Carlo Tree Search and sparse distributed memory

SDM-Go: an Agent for Go with an Improved Search Process Base...

引用

IEEE 16th International Conference on Computational Science and Engineering (CSE)

作者： Aguiar, Matheus Araujo Silva Julia, Rita Maria Univ Fed Uberlandia Dept Comp Sci BR-38400 Uberlandia MG Brazil

ISBN: (纸本)9781479948970

This paper describes the player agent for Go named SDM-Go. It uses sparse distributed memory (SDM) as an additional resource to improve the classical Monte-Carlo (MC) simulation based search used by many current top player agents. In this process, the search tree used to select the best move is constructed according to the values of the game boards. The SDM-Go is built upon the Fuego framework. The contributions here are: the use of a SDM to reduce the number of MC simulations and to increase the accuracy of the calculus of the values corresponding to the game boards (nodes) that keep a certain level of similarity with some node of the SDM;the implementation of a bit based representation for the game boards such as not to compromise the performance of the system when checking the similarities among them and;to extend the use of the results of the simulations to update the values of the nodes of the SDM. The use of the SDM represents an approach independent of domain, what reduces the supervised character of the traditional MC simulations. The results obtained in tournaments against the well known open-source agent Fuego confirm the benefits provided by this approach.

关键词： Game Agent for Go sparse distributed memory Monte Carlo Tree Search Simulations

来源：评论

学校读者我要写书评

暂无评论

In-memory Computing Architectures for sparse distributed memory

引用

IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2016年第4期10卷 855-863页

作者： Kang, Mingu Shanbhag, Naresh R. Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

This paper presents an energy-efficient and high-throughput architecture for sparse distributed memory (SDM)-a computational model of the human brain [1]. The proposed SDM architecture is based on the recently proposed in-memory computing kernel for machine learning applications called Compute memory (CM) [2], [3]. CM achieves energy and throughput efficiencies by deeply embedding computation into the memory array. SDM-specific techniques such as hierarchical binary decision (HBD) are employed to reduce the delay and energy further. The CM-based SDM (CM-SDM) is a mixed-signal circuit, and hence circuit-aware behavioral, energy, and delay models in a 65 nm CMOS process are developed in order to predict system performance of SDM architectures in the auto- and hetero-associative modes. The delay and energy models indicate that CM-SDM, in general, can achieve up to 25 x and 12 x delay and energy reduction, respectively, over conventional SDM. When classifying 16 x16 binary images with high noise levels (input bad pixel ratios: 15%-25%) into nine classes, all SDM architectures are able to generate output bad pixel ratios (B-o) <= 2%. The CM-SDM exhibits negligible loss in accuracy, i.e., its B-o degradation is within 0.4% as compared to that of the conventional SDM.

关键词： Associative memory brain-inspired computing Compute memory machine learning pattern recognition sparse distributed memory

来源：评论

学校读者我要写书评

暂无评论

Analogical mapping and inference with binary spatter codes and sparse distributed memory

Analogical mapping and inference with binary spatter codes a...

引用

International Joint Conference on Neural Networks

作者： Blerim Emruli Ross W. Gayler Fredrik Sandin EISLAB at the Lule? University of Technology 97187 Lule? Sweden Honorary Associate of La Trobe University Bundoora VIC 3086 Australia

ISBN: (纸本)9781467361279

Analogy-making is a key function of human cognition. Therefore, the development of computational models of analogy that automatically learn from examples can lead to significant advances in cognitive systems. Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models. Vector symbolic architectures (VSAs) are a class of connectionist models for the representation and manipulation of compositional structures, which can be used to model analogy. We study a novel VSA network for the analogical mapping of compositional structures, which integrates an associative memory known as sparse distributed memory (SDM). The SDM enables non-commutative binding of compositional structures, which makes it possible to predict novel patterns in sequences. To demonstrate this property we apply the network to a commonly used intelligence test called Raven's Progressive Matrices. We present results of simulation experiments for the Raven's task and calculate the probability of prediction error at 95% confidence level. We find that non-commutative binding requires sparse activation of the SDM and that 10-20% concept-specific activation of neurons is optimal. The optimal dimensionality of the binary distributed representations of the VSA is of the order 10~4, which is comparable with former results and the average synapse count of neurons in the cerebral cortex.

关键词： sparse distributed memory VSAs SDM

来源：评论

学校读者我要写书评

暂无评论

A Scalable Cluster-based Hierarchical Hardware Accelerator for a Cortically Inspired Algorithm

引用

ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS 2021年第4期17卷 1–29页

作者： Dey, Sumon Baker, Lee Schabel, Joshua Li, Weifu Franzon, Paul D. North Carolina State Univ Raleigh NC 27695 USA

This article describes a scalable, configurable and cluster-based hierarchical hardware accelerator through custom hardware architecture for sparsey, a cortical learning algorithm. sparsey is inspired by the operation of the human cortex and uses a sparse distributed Representation to enable unsupervised learning and inference in the same algorithm. A distributed on-chip memory organization is designed and implemented in custom hardware to improve memory bandwidth and accelerate the memory read/write operations for synaptic weight matrices. Bit-level data are processed from distributed on-chip memory and custom multiply-accumulate hardware is implemented for binary and fixed-point multiply-accumulation operations. The fixed-point arithmetic and fixed-point storage are also adapted in this implementation. At 16 nm, the custom hardware of sparsey achieved an overall 24.39x speedup, 353.12x energy efficiency per frame, and 1.43x reduction in silicon area against a state-of-the-art GPU.

关键词： Neuromorphic computing accelerator cortical processor hierarchical temporal memory sparse distributed memory

来源：评论

学校读者我要写书评

暂无评论

Vector space architecture for emergent interoperability of systems by learning from demonstration

引用

BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES 2015年 11卷 53-64页

作者： Emruli, Blerim Sandin, Fredrik Delsing, Jerker Lulea Univ Technol EISLAB S-97187 Lulea Sweden

The rapid integration of physical systems with cyberspace infrastructure, the so-called Internet of Things, is likely to have a significant effect on how people interact with the physical environment and design information and communication systems. Internet-connected systems are expected to vastly outnumber people on the planet in the near future, leading to grand challenges in software engineering and automation in application domains involving complex and evolving systems. Several decades of artificial intelligence research suggests that conventional approaches to making such systems automatically interoperable using handcrafted "semantic" descriptions of services and information are difficult to apply. In this paper we outline a bioinspired learning approach to creating interoperable systems, which does not require handcrafted semantic descriptions and rules. Instead, the idea is that a functioning system (of systems) can emerge from an initial pseudorandom state through learning from examples, provided that each component conforms to a set of information coding rules. We combine a binary vector symbolic architecture (V5A) with an associative memory known as sparse distributed memory (SDM) to model context-dependent prediction by learning from examples. We present simulation results demonstrating that the proposed architecture can enable system interoperability by learning, for example by human demonstration. (C) 2015 Published by Elsevier B.V.

关键词： Communications Interoperability Vector symbolic architecture sparse distributed memory Artificial intelligence System of systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：